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Python实现数据可视化案例分析

作者:biyezuopinvip

这篇文章主要介绍了Python实现数据可视化案例分析,文章围绕主题展开详细的内容介绍,具有一定的参考价值,需要的小伙伴可以参考一下

1. 问题描述

对右图进行修改:

展示 10-15 年 PM 指数月平均数据的变化情况,一幅图中有 6 条曲线,每年 1 条曲线。

2. 实验环境

Microsoft Windows 10 版本18363

​ PyCharm 2020.2.1 (Community Edition)

​ Python 3.8(Scrapy 2.4.0 + numpy 1.19.4 + pandas 1.1.4 + matplotlib 3.3.3)

3. 实验步骤及结果

对右图进行修改:

from matplotlib import pyplot as plt
import numpy as np

fig, ax = plt.subplots()
plt.style.use('classic')
plt.title("square numbers")

ax.set_xlim(-11, 11)
ax.set_ylim(0, 100)

x = np.array(range(-10, 11))
y = x * x
rect1 = plt.bar(x, y)
for r in rect1:
    ax.text(r.get_x(), r.get_height() / 2, r.get_height())
plt.show()

如图使用 classic 风格,x 轴数据为[-10, 10]的整数,构造的函数为 y=x2,显示位置并将其将数值改到了柱形图内部垂直居中的位置。

对成绩数据 data1402.csv 进行分段统计:每 5 分作为一个分数段,展示出每个分数段的人数直方图。

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd

df = pd.read_csv("./data1402.csv", encoding='utf-8', dtype=str)
df = pd.DataFrame(df, columns=['score'], dtype=np.float)
section = np.array(range(0, 105, 5))
result = pd.cut(df['score'], section)
count = pd.value_counts(result, sort=False)
fig, ax = plt.subplots()
plt.style.use('classic')
ax.set_xlim(0, 100)
rect1 = plt.bar(np.arange(2.5, 100, 5), count, width=5)
for r in rect1:
    ax.text(r.get_x(), r.get_height(), r.get_height())
plt.show()

自行创建出 10 个学生的 3 个学期排名数据,并通过直方图进行对比展示。

import random

semester1 = np.arange(1, 11)
semester2 = np.arange(1, 11)
semester3 = np.arange(1, 11)

random.shuffle(semester1)
random.shuffle(semester2)
random.shuffle(semester3)
df = pd.DataFrame({'semester1':semester1, 'semester2':semester2, 'semester3':semester3})
print(df)
df.to_csv("data1403.csv", encoding="utf-8")

使用如上代码创建出随机的排名数据。

df = pd.read_csv("./data1403.csv", encoding='utf-8', dtype=str)
df = pd.DataFrame(df, columns=['semester1', 'semester2', 'semester3'], dtype=np.int)

df['total'] = (df['semester1'] + df['semester2'] + df['semester3']) / 3
df = df.sort_values('total')

fig, ax = plt.subplots()
plt.style.use('classic')
plt.title('RANK')
width = 0.2
x = np.array(range(0, 10))
rect1 = ax.bar(x-2*width, df['semester1'], width=width, label='semester1')
rect2 = ax.bar(x-width, df['semester2'], width=width, label='semester2')
rect3 = ax.bar(x, df['semester3'], width=width, label='semester3')
for r in rect1:
    ax.text(r.get_x(), r.get_height(), r.get_height())
for r in rect2:
    ax.text(r.get_x(), r.get_height(), r.get_height())
for r in rect3:
    ax.text(r.get_x(), r.get_height(), r.get_height())
plt.legend(ncol=1)
plt.show()

如上代码绘图:

线图 :

import numpy as np
from matplotlib import pyplot as plt

x = np.linspace(-5 * np.pi, 5 * np.pi, 500)
y1 = 3 * np.cos(x)
y2 = np.sin(4*x)

fig, ax = plt.subplots()
plt.style.use('classic')
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines['bottom'].set_position(('data',0))
ax.xaxis.set_ticks_position('bottom')
ax.spines['left'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
plt.plot(x, y1, color='blue', linestyle='-', label='y=3cosx')
plt.plot(x, y2, color='red', linestyle='-', label='y=sin3x')
plt.legend()
plt.show()

用线图展示北京空气质量数据

展示 10-15 年 PM 指数月平均数据的变化情况,一幅图中有 6 条曲线,每年 1 条曲线。

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
orig_df = pd.read_csv("./BeijingPM20100101_20151231.csv", encoding='utf-8', dtype=str)
orig_df = pd.DataFrame(orig_df, columns=['year', 'month', 'PM_US Post'])
df = orig_df.dropna(0, how='any')
df['month'] = df['month'].astype(int)
df['year'] = df['year'].astype(int)
df['PM_US Post'] = df['PM_US Post'].astype(int)
df.reset_index(drop=True, inplace=True)
num = len(df)
section = np.arange(1, 13)
record = 0
fig, ax = plt.subplots()
plt.style.use('classic')
plt.title("2010-2015 Beijing average PM2.5(from PM_US Post) per month")

for nowyear in range(2010, 2016):
    i = record
    result = [0 for i in range(13)]
    nowsum = 0
    cntday = 0
    nowmonth = 1
    while i < num:
        if df['month'][i] == nowmonth:
            cntday = cntday + 1
            nowsum = nowsum + df['PM_US Post'][i]
        else:
            if df['year'][i] != nowyear:
                record = i
                result[nowmonth] = nowsum / cntday
                break
            result[nowmonth] = nowsum / cntday
            cntday = 1
            nowsum = df['PM_US Post'][i]
            nowmonth = df['month'][i]
        i = i + 1
    result = result[1:]
    #
    x = np.array(range(1, 13))
    plt.plot(x, result, linestyle='-', label=str(nowyear))
plt.legend()
plt.show()

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